Tired of Reading Long Articles? Text Summarization will make your task easier!

This article was published as a part of the Data Science Blogathon. Introduction Millions of web pages and websites exist on the Internet today. Going through a vast amount of content becomes very difficult to extract information on a certain topic. Google will filter the search results and give you the top ten search results, but often you are unable to find the right content that you need. There is a lot of redundant and overlapping data in the articles […]

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A Quick Guide to Text Cleaning Using the nltk Library

This article was published as a part of the Data Science Blogathon. Introduction NLTK is a string processing library that takes strings as input. The output is in the form of either a string or lists of strings. This library provides a lot of algorithms that helps majorly in the learning purpose. One can compare among different variants of outputs. There are other libraries as well like spaCy, CoreNLP, PyNLPI, Polyglot. NLTK and spaCy are most widely used. Spacy works […]

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Words that matter! A Simple Guide to Keyword Extraction in Python

This article was published as a part of the Data Science Blogathon. Introduction Unstructured data contains a plethora of information. It is like energy when harnessed, will create high value for its stakeholders. A lot of work is already being done in this area by various companies. There is no doubt that the unstructured data is noisy and significant work has to be done to clean, analyze, and make them meaningful to use. This article talks about an area which […]

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Text Mining Simplified – IPL 2020 Tweet Analysis with R

This article was published as a part of the Data Science Blogathon. Introduction Text mining utilizes different AI technologies to automatically process data and generate valuable insights, enabling companies to make data-driven decisions. Text mining identifies facts, relationships, and assertions that would otherwise remain buried in the mass of textual big data. Once extracted, this information is converted into a structured form that can be further analyzed, or presented directly using clustered HTML tables, mind maps, charts, etc. Advantages of […]

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Create a Pipeline to Perform Sentiment Analysis using NLP

This article was published as a part of the Data Science Blogathon. Overview Every basic fundamental and building block which is required for Sentiment Analysis. I’ve used an easy approach to explain all the basic concepts so that even a beginner reader would be able to get a thorough understanding of all the concepts. Topics: Preprocessing text, Vocabulary Corpus, Feature Extraction (Sparse Representation and Frequency Dictionary), Logistic Regression model for sentiment analysis.   Sentiment Analysis is a supervised Machine Learning […]

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Handling Imbalanced Data – Machine Learning, Computer Vision and NLP

This article was published as a part of the Data Science Blogathon. Introduction: In the real world, the data we gather will be heavily imbalanced most of the time. so, what is an Imbalanced Dataset?. The training samples are not equally distributed across the target classes.  For instance, if we take the case of the personal loan classification problem, it is effortless to get the ‘not approved’ data, in contrast to,  ‘approved’ details. As a result, the model is more […]

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Simple Text Multi Classification Task Using Keras BERT

This article was published as a part of the Data Science Blogathon. Introduction BERT is a really powerful language representation model that has been a big milestone in the field of NLP. It has greatly increased our capacity to do transfer learning in NLP. It comes with great promise to solve a wide variety of NLP tasks. Definitely you will gain great knowledge by the end of this article, keep reading. I am sure you will get good hands-on experience […]

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NLP Applications in Support Call Centers

This article was published as a part of the Data Science Blogathon. Introduction This article is in continuation of my previous article on using Machine learning in Support environments. I shared my views on, how using simple python code we can enrich our call centers/support division activities in our own organization or customer organization. In that article, I shared an insight into what and how we can make a difference to the current environment using ML in giving better service […]

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A Quick History of Neural Networks

This article is part of the Data Science Blogathon. Introduction Neural networks are ubiquitous right now. Organizations are splurging money on hardware and talent to ensure they can build the most complex neural networks and bring out the best deep learning solutions. Although Deep Learning is a fairly old subset of machine learning, it didn’t get its due recognition until the early 2010s. Today, it has taken the world by storm and captured public attention in a way that very […]

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